{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "请先前往oanda官网注册账号获取token和ID。由于计算复杂度，目前只支持美元货币对。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[参照此链接，新建并将oandakey.py文件添加到pythono搜索目录](https://python3-cookbook.readthedocs.io/zh_CN/latest/c10/p09_add_directories_to_sys_path.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# oandakey.py  \n",
    "access_token = ''\n",
    "accountID = ''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先要安装oanda api\n",
    "\n",
    "`pip install oandapyV20`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start!\n",
      "['EUR_USD'],M30 all set!!!!!\n",
      "<<EUR_USD, M30>> has been drop 76961 duplicates!\n"
     ]
    }
   ],
   "source": [
    "# 多线程保存数据\n",
    "from oandakey import access_token, accountID\n",
    "from OnePy.builtin_module.mongodb_saver import oanda_saver\n",
    "from OnePy.builtin_module.mongodb_saver.utils import MongoDBFunc\n",
    "from OnePy.utils.awesome_func import run_multiprocessing, run_multithreading\n",
    "\n",
    "# 多个\n",
    "ticker_list = ['EUR_USD', 'AUD_USD', 'GBP_USD', 'USD_CAD', 'USD_JPY']\n",
    "ticker_list = ['EUR_USD']\n",
    "\n",
    "# period_list = ['M5',  'M30', 'H1', 'H2', 'H3', 'H4', 'H6', 'H12', 'D', 'W']\n",
    "period_list = ['M30']\n",
    "\n",
    "\n",
    "oanda_saver.multi_oanda_candles_to_mongodb(accountID, access_token,\n",
    "                                           ticker_list,\n",
    "                                           period_list,\n",
    "                                           fromdate='2015-01-01')\n",
    "\n",
    "MongoDBFunc().drop_duplicates(ticker_list, period_list, 'oanda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import OnePy as op\n",
    "from OnePy.custom_module.cleaner_sma import SMA\n",
    "\n",
    "\n",
    "class SmaStrategy(op.StrategyBase):\n",
    "\n",
    "    def __init__(self):\n",
    "\n",
    "        super().__init__()\n",
    "        self.sma1 = SMA(3, 40).calculate\n",
    "        self.sma2 = SMA(5, 40).calculate\n",
    "\n",
    "    def handle_bar(self):\n",
    "        for ticker in self.env.tickers:\n",
    "\n",
    "            if self.sma1(ticker) > self.sma2(ticker):\n",
    "\n",
    "                self.buy(100, ticker, takeprofit=15,\n",
    "                         stoploss=100)\n",
    "            else:\n",
    "                self.sell(100, ticker)\n",
    "\n",
    "                \n",
    "\n",
    "START, END = \"2016-01-05\", \"2016-02-05\"\n",
    "FREQUENCY = \"M30\"\n",
    "TICKER_LIST = [\"EUR_USD\"]\n",
    "INITIAL_CASH = 20000\n",
    "\n",
    "SmaStrategy()\n",
    "\n",
    "go = op.backtest.forex(TICKER_LIST, FREQUENCY, INITIAL_CASH, START, END, \"oanda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "正在初始化OnePy\n",
      "=============== OnePy初始化成功！ ===============\n",
      "开始寻找OnePiece之旅~~~\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> [97%]\n",
      "\n",
      "+--------------------------------+\n",
      "|Fromdate           |  2016-01-05|\n",
      "|Todate             |  2016-02-05|\n",
      "|Initial_Value      |   $20000.00|\n",
      "|Final_Value        |   $21973.31|\n",
      "|Total_Return       |      9.867%|\n",
      "|Max_Drawdown       |      4.143%|\n",
      "|Max_Duration       |     22 days|\n",
      "|Max_Drawdown_Date  |  2016-01-31|\n",
      "|Sharpe_Ratio       |        3.79|\n",
      "+--------------------------------+\n"
     ]
    }
   ],
   "source": [
    "go.sunny(show_process=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chandler/anaconda/lib/python3.6/site-packages/matplotlib/figure.py:2362: UserWarning:\n",
      "\n",
      "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAALICAYAAAAqrmQHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQAkhhHBLUqCEEEK4pf8AvSFKmuC6EIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "go.output.plot('EUR_USD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>entry_date</th>\n",
       "      <th>entry_price</th>\n",
       "      <th>entry_type</th>\n",
       "      <th>size</th>\n",
       "      <th>exit_date</th>\n",
       "      <th>exit_price</th>\n",
       "      <th>exit_type</th>\n",
       "      <th>pl_points</th>\n",
       "      <th>re_pnl</th>\n",
       "      <th>comm</th>\n",
       "      <th>holding_period</th>\n",
       "      <th>drawdown</th>\n",
       "      <th>run_up</th>\n",
       "      <th>returns_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 01:00:00</td>\n",
       "      <td>1.08348</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-05 02:30:00</td>\n",
       "      <td>1.08240</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00108</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1:30:00</td>\n",
       "      <td>0.001791</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>-0.000997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 01:30:00</td>\n",
       "      <td>1.08248</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-05 03:00:00</td>\n",
       "      <td>1.08170</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00078</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1:30:00</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>-0.000721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 04:30:00</td>\n",
       "      <td>1.08226</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-05 07:30:00</td>\n",
       "      <td>1.08064</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00162</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>0.015</td>\n",
       "      <td>3:00:00</td>\n",
       "      <td>0.001709</td>\n",
       "      <td>0.001294</td>\n",
       "      <td>-0.001497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 05:00:00</td>\n",
       "      <td>1.08296</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-05 08:00:00</td>\n",
       "      <td>1.08088</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00208</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>0.015</td>\n",
       "      <td>3:00:00</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>-0.001921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 05:30:00</td>\n",
       "      <td>1.08312</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-05 08:30:00</td>\n",
       "      <td>1.08060</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00252</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>0.015</td>\n",
       "      <td>3:00:00</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>-0.002327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 06:00:00</td>\n",
       "      <td>1.08301</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-05 09:00:00</td>\n",
       "      <td>1.07898</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00403</td>\n",
       "      <td>-4.03</td>\n",
       "      <td>0.015</td>\n",
       "      <td>3:00:00</td>\n",
       "      <td>0.004377</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>-0.003721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 06:30:00</td>\n",
       "      <td>1.08272</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-05 09:30:00</td>\n",
       "      <td>1.07898</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00374</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>0.015</td>\n",
       "      <td>3:00:00</td>\n",
       "      <td>0.005034</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>-0.003454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 17:00:00</td>\n",
       "      <td>1.07355</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-05 19:30:00</td>\n",
       "      <td>1.07369</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00014</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.015</td>\n",
       "      <td>2:30:00</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.001220</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 17:30:00</td>\n",
       "      <td>1.07459</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-05 20:00:00</td>\n",
       "      <td>1.07438</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00021</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.015</td>\n",
       "      <td>2:30:00</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>-0.000195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 18:00:00</td>\n",
       "      <td>1.07439</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 00:30:00</td>\n",
       "      <td>1.07446</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.015</td>\n",
       "      <td>6:30:00</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 18:30:00</td>\n",
       "      <td>1.07334</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 01:00:00</td>\n",
       "      <td>1.07448</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00114</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.015</td>\n",
       "      <td>6:30:00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.003689</td>\n",
       "      <td>0.001062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 20:00:00</td>\n",
       "      <td>1.07438</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 01:30:00</td>\n",
       "      <td>1.07674</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00236</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.015</td>\n",
       "      <td>5:30:00</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>0.002718</td>\n",
       "      <td>0.002197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 20:30:00</td>\n",
       "      <td>1.07499</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 03:30:00</td>\n",
       "      <td>1.07516</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00017</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.015</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.000158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 21:00:00</td>\n",
       "      <td>1.07503</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 04:00:00</td>\n",
       "      <td>1.07456</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00047</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.015</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>0.001033</td>\n",
       "      <td>0.002112</td>\n",
       "      <td>-0.000437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 21:30:00</td>\n",
       "      <td>1.07467</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 04:30:00</td>\n",
       "      <td>1.07486</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00019</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.015</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.002447</td>\n",
       "      <td>0.000177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 22:00:00</td>\n",
       "      <td>1.07516</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 05:00:00</td>\n",
       "      <td>1.07492</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00024</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.015</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.001990</td>\n",
       "      <td>-0.000223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 22:30:00</td>\n",
       "      <td>1.07538</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 05:30:00</td>\n",
       "      <td>1.07444</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00094</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>0.015</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>0.001785</td>\n",
       "      <td>-0.000874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 23:00:00</td>\n",
       "      <td>1.07554</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 06:00:00</td>\n",
       "      <td>1.07444</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00110</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>0.015</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>0.001506</td>\n",
       "      <td>0.001636</td>\n",
       "      <td>-0.001023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-05 23:30:00</td>\n",
       "      <td>1.07510</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 06:30:00</td>\n",
       "      <td>1.07519</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.015</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>0.001098</td>\n",
       "      <td>0.002046</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 01:30:00</td>\n",
       "      <td>1.07616</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 07:30:00</td>\n",
       "      <td>1.07342</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00274</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>0.015</td>\n",
       "      <td>6:00:00</td>\n",
       "      <td>0.003931</td>\n",
       "      <td>0.000836</td>\n",
       "      <td>-0.002546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 02:00:00</td>\n",
       "      <td>1.07642</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 08:00:00</td>\n",
       "      <td>1.07300</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00342</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>0.015</td>\n",
       "      <td>6:00:00</td>\n",
       "      <td>0.004171</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>-0.003177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 02:30:00</td>\n",
       "      <td>1.07641</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 08:30:00</td>\n",
       "      <td>1.07320</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00321</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>0.015</td>\n",
       "      <td>6:00:00</td>\n",
       "      <td>0.004162</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>-0.002982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 06:30:00</td>\n",
       "      <td>1.07450</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 09:00:00</td>\n",
       "      <td>1.07356</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00094</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>0.015</td>\n",
       "      <td>2:30:00</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>-0.000875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 09:30:00</td>\n",
       "      <td>1.07346</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 10:30:00</td>\n",
       "      <td>1.07328</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00018</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1:00:00</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>-0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 10:30:00</td>\n",
       "      <td>1.07357</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 14:00:00</td>\n",
       "      <td>1.07396</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00039</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.015</td>\n",
       "      <td>3:30:00</td>\n",
       "      <td>0.001910</td>\n",
       "      <td>0.002263</td>\n",
       "      <td>0.000363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 11:00:00</td>\n",
       "      <td>1.07440</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 14:30:00</td>\n",
       "      <td>1.07392</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>-0.00048</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.015</td>\n",
       "      <td>3:30:00</td>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>-0.000447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 11:30:00</td>\n",
       "      <td>1.07490</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 19:00:00</td>\n",
       "      <td>1.07558</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00068</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.015</td>\n",
       "      <td>7:30:00</td>\n",
       "      <td>0.003144</td>\n",
       "      <td>0.004689</td>\n",
       "      <td>0.000633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 12:00:00</td>\n",
       "      <td>1.07547</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 22:00:00</td>\n",
       "      <td>1.07807</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00260</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.015</td>\n",
       "      <td>10:00:00</td>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.004156</td>\n",
       "      <td>0.002418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 12:30:00</td>\n",
       "      <td>1.07430</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 22:30:00</td>\n",
       "      <td>1.07805</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00375</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.015</td>\n",
       "      <td>10:00:00</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.005250</td>\n",
       "      <td>0.003491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-01-06 13:00:00</td>\n",
       "      <td>1.07430</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>2016-01-06 23:00:00</td>\n",
       "      <td>1.07742</td>\n",
       "      <td>Market Sell</td>\n",
       "      <td>0.00312</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.015</td>\n",
       "      <td>10:00:00</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.005250</td>\n",
       "      <td>0.002904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-03 19:30:00</td>\n",
       "      <td>1.10937</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.01052</td>\n",
       "      <td>10.52</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1008 days, 3:14:24.250020</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-03 21:00:00</td>\n",
       "      <td>1.11015</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00974</td>\n",
       "      <td>9.74</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1008 days, 1:44:24.252845</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-03 21:30:00</td>\n",
       "      <td>1.11056</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00933</td>\n",
       "      <td>9.33</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1008 days, 1:14:24.255557</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 00:00:00</td>\n",
       "      <td>1.11102</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00887</td>\n",
       "      <td>8.87</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 22:44:24.258263</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 00:30:00</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00878</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 22:14:24.261205</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 01:00:00</td>\n",
       "      <td>1.10924</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.01065</td>\n",
       "      <td>10.65</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 21:44:24.264659</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 03:30:00</td>\n",
       "      <td>1.10913</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.01076</td>\n",
       "      <td>10.76</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 19:14:24.267997</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 04:00:00</td>\n",
       "      <td>1.10850</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.01139</td>\n",
       "      <td>11.39</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 18:44:24.271346</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 04:30:00</td>\n",
       "      <td>1.10871</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.01118</td>\n",
       "      <td>11.18</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 18:14:24.274196</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 08:00:00</td>\n",
       "      <td>1.11265</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00724</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 14:44:24.276903</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 08:30:00</td>\n",
       "      <td>1.11331</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00658</td>\n",
       "      <td>6.58</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 14:14:24.279598</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 09:00:00</td>\n",
       "      <td>1.11577</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>4.12</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 13:44:24.282315</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 09:30:00</td>\n",
       "      <td>1.11628</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>3.61</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 13:14:24.285016</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 10:00:00</td>\n",
       "      <td>1.11611</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00378</td>\n",
       "      <td>3.78</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 12:44:24.287718</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 10:30:00</td>\n",
       "      <td>1.11817</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00172</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 12:14:24.290421</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 11:00:00</td>\n",
       "      <td>1.11838</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00151</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 11:44:24.293186</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 11:30:00</td>\n",
       "      <td>1.11718</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00271</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 11:14:24.295881</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 12:00:00</td>\n",
       "      <td>1.11797</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00192</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 10:44:24.298576</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 13:00:00</td>\n",
       "      <td>1.12202</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.00213</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 9:44:24.301272</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 13:30:00</td>\n",
       "      <td>1.12026</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.00037</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 9:14:24.304001</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 14:00:00</td>\n",
       "      <td>1.11884</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00105</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 8:44:24.306750</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 17:00:00</td>\n",
       "      <td>1.11881</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00108</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 5:44:24.309452</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 17:30:00</td>\n",
       "      <td>1.11974</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00015</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 5:14:24.312156</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 18:00:00</td>\n",
       "      <td>1.12050</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.00061</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 4:44:24.314855</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 18:30:00</td>\n",
       "      <td>1.12113</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.00124</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 4:14:24.317541</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 19:00:00</td>\n",
       "      <td>1.12208</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.00219</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 3:44:24.320242</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 19:30:00</td>\n",
       "      <td>1.12120</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.00131</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 3:14:24.322986</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 20:00:00</td>\n",
       "      <td>1.12023</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.00034</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 2:44:24.325686</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 21:30:00</td>\n",
       "      <td>1.12070</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.00081</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 1:14:24.328395</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>EUR_USD</td>\n",
       "      <td>2016-02-04 22:00:00</td>\n",
       "      <td>1.12008</td>\n",
       "      <td>Market Buy</td>\n",
       "      <td>100</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.00019</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1007 days, 0:44:24.331094</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>584 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ticker           entry_date  entry_price  entry_type  size  \\\n",
       "0    EUR_USD  2016-01-05 01:00:00      1.08348  Market Buy   100   \n",
       "1    EUR_USD  2016-01-05 01:30:00      1.08248  Market Buy   100   \n",
       "2    EUR_USD  2016-01-05 04:30:00      1.08226  Market Buy   100   \n",
       "3    EUR_USD  2016-01-05 05:00:00      1.08296  Market Buy   100   \n",
       "4    EUR_USD  2016-01-05 05:30:00      1.08312  Market Buy   100   \n",
       "5    EUR_USD  2016-01-05 06:00:00      1.08301  Market Buy   100   \n",
       "6    EUR_USD  2016-01-05 06:30:00      1.08272  Market Buy   100   \n",
       "7    EUR_USD  2016-01-05 17:00:00      1.07355  Market Buy   100   \n",
       "8    EUR_USD  2016-01-05 17:30:00      1.07459  Market Buy   100   \n",
       "9    EUR_USD  2016-01-05 18:00:00      1.07439  Market Buy   100   \n",
       "10   EUR_USD  2016-01-05 18:30:00      1.07334  Market Buy   100   \n",
       "11   EUR_USD  2016-01-05 20:00:00      1.07438  Market Buy   100   \n",
       "12   EUR_USD  2016-01-05 20:30:00      1.07499  Market Buy   100   \n",
       "13   EUR_USD  2016-01-05 21:00:00      1.07503  Market Buy   100   \n",
       "14   EUR_USD  2016-01-05 21:30:00      1.07467  Market Buy   100   \n",
       "15   EUR_USD  2016-01-05 22:00:00      1.07516  Market Buy   100   \n",
       "16   EUR_USD  2016-01-05 22:30:00      1.07538  Market Buy   100   \n",
       "17   EUR_USD  2016-01-05 23:00:00      1.07554  Market Buy   100   \n",
       "18   EUR_USD  2016-01-05 23:30:00      1.07510  Market Buy   100   \n",
       "19   EUR_USD  2016-01-06 01:30:00      1.07616  Market Buy   100   \n",
       "20   EUR_USD  2016-01-06 02:00:00      1.07642  Market Buy   100   \n",
       "21   EUR_USD  2016-01-06 02:30:00      1.07641  Market Buy   100   \n",
       "22   EUR_USD  2016-01-06 06:30:00      1.07450  Market Buy   100   \n",
       "23   EUR_USD  2016-01-06 09:30:00      1.07346  Market Buy   100   \n",
       "24   EUR_USD  2016-01-06 10:30:00      1.07357  Market Buy   100   \n",
       "25   EUR_USD  2016-01-06 11:00:00      1.07440  Market Buy   100   \n",
       "26   EUR_USD  2016-01-06 11:30:00      1.07490  Market Buy   100   \n",
       "27   EUR_USD  2016-01-06 12:00:00      1.07547  Market Buy   100   \n",
       "28   EUR_USD  2016-01-06 12:30:00      1.07430  Market Buy   100   \n",
       "29   EUR_USD  2016-01-06 13:00:00      1.07430  Market Buy   100   \n",
       "..       ...                  ...          ...         ...   ...   \n",
       "554  EUR_USD  2016-02-03 19:30:00      1.10937  Market Buy   100   \n",
       "555  EUR_USD  2016-02-03 21:00:00      1.11015  Market Buy   100   \n",
       "556  EUR_USD  2016-02-03 21:30:00      1.11056  Market Buy   100   \n",
       "557  EUR_USD  2016-02-04 00:00:00      1.11102  Market Buy   100   \n",
       "558  EUR_USD  2016-02-04 00:30:00      1.11111  Market Buy   100   \n",
       "559  EUR_USD  2016-02-04 01:00:00      1.10924  Market Buy   100   \n",
       "560  EUR_USD  2016-02-04 03:30:00      1.10913  Market Buy   100   \n",
       "561  EUR_USD  2016-02-04 04:00:00      1.10850  Market Buy   100   \n",
       "562  EUR_USD  2016-02-04 04:30:00      1.10871  Market Buy   100   \n",
       "563  EUR_USD  2016-02-04 08:00:00      1.11265  Market Buy   100   \n",
       "564  EUR_USD  2016-02-04 08:30:00      1.11331  Market Buy   100   \n",
       "565  EUR_USD  2016-02-04 09:00:00      1.11577  Market Buy   100   \n",
       "566  EUR_USD  2016-02-04 09:30:00      1.11628  Market Buy   100   \n",
       "567  EUR_USD  2016-02-04 10:00:00      1.11611  Market Buy   100   \n",
       "568  EUR_USD  2016-02-04 10:30:00      1.11817  Market Buy   100   \n",
       "569  EUR_USD  2016-02-04 11:00:00      1.11838  Market Buy   100   \n",
       "570  EUR_USD  2016-02-04 11:30:00      1.11718  Market Buy   100   \n",
       "571  EUR_USD  2016-02-04 12:00:00      1.11797  Market Buy   100   \n",
       "572  EUR_USD  2016-02-04 13:00:00      1.12202  Market Buy   100   \n",
       "573  EUR_USD  2016-02-04 13:30:00      1.12026  Market Buy   100   \n",
       "574  EUR_USD  2016-02-04 14:00:00      1.11884  Market Buy   100   \n",
       "575  EUR_USD  2016-02-04 17:00:00      1.11881  Market Buy   100   \n",
       "576  EUR_USD  2016-02-04 17:30:00      1.11974  Market Buy   100   \n",
       "577  EUR_USD  2016-02-04 18:00:00      1.12050  Market Buy   100   \n",
       "578  EUR_USD  2016-02-04 18:30:00      1.12113  Market Buy   100   \n",
       "579  EUR_USD  2016-02-04 19:00:00      1.12208  Market Buy   100   \n",
       "580  EUR_USD  2016-02-04 19:30:00      1.12120  Market Buy   100   \n",
       "581  EUR_USD  2016-02-04 20:00:00      1.12023  Market Buy   100   \n",
       "582  EUR_USD  2016-02-04 21:30:00      1.12070  Market Buy   100   \n",
       "583  EUR_USD  2016-02-04 22:00:00      1.12008  Market Buy   100   \n",
       "\n",
       "               exit_date  exit_price    exit_type  pl_points  re_pnl   comm  \\\n",
       "0    2016-01-05 02:30:00     1.08240  Market Sell   -0.00108   -1.08  0.015   \n",
       "1    2016-01-05 03:00:00     1.08170  Market Sell   -0.00078   -0.78  0.015   \n",
       "2    2016-01-05 07:30:00     1.08064  Market Sell   -0.00162   -1.62  0.015   \n",
       "3    2016-01-05 08:00:00     1.08088  Market Sell   -0.00208   -2.08  0.015   \n",
       "4    2016-01-05 08:30:00     1.08060  Market Sell   -0.00252   -2.52  0.015   \n",
       "5    2016-01-05 09:00:00     1.07898  Market Sell   -0.00403   -4.03  0.015   \n",
       "6    2016-01-05 09:30:00     1.07898  Market Sell   -0.00374   -3.74  0.015   \n",
       "7    2016-01-05 19:30:00     1.07369  Market Sell    0.00014    0.14  0.015   \n",
       "8    2016-01-05 20:00:00     1.07438  Market Sell   -0.00021   -0.21  0.015   \n",
       "9    2016-01-06 00:30:00     1.07446  Market Sell    0.00007    0.07  0.015   \n",
       "10   2016-01-06 01:00:00     1.07448  Market Sell    0.00114    1.14  0.015   \n",
       "11   2016-01-06 01:30:00     1.07674  Market Sell    0.00236    2.36  0.015   \n",
       "12   2016-01-06 03:30:00     1.07516  Market Sell    0.00017    0.17  0.015   \n",
       "13   2016-01-06 04:00:00     1.07456  Market Sell   -0.00047   -0.47  0.015   \n",
       "14   2016-01-06 04:30:00     1.07486  Market Sell    0.00019    0.19  0.015   \n",
       "15   2016-01-06 05:00:00     1.07492  Market Sell   -0.00024   -0.24  0.015   \n",
       "16   2016-01-06 05:30:00     1.07444  Market Sell   -0.00094   -0.94  0.015   \n",
       "17   2016-01-06 06:00:00     1.07444  Market Sell   -0.00110   -1.10  0.015   \n",
       "18   2016-01-06 06:30:00     1.07519  Market Sell    0.00009    0.09  0.015   \n",
       "19   2016-01-06 07:30:00     1.07342  Market Sell   -0.00274   -2.74  0.015   \n",
       "20   2016-01-06 08:00:00     1.07300  Market Sell   -0.00342   -3.42  0.015   \n",
       "21   2016-01-06 08:30:00     1.07320  Market Sell   -0.00321   -3.21  0.015   \n",
       "22   2016-01-06 09:00:00     1.07356  Market Sell   -0.00094   -0.94  0.015   \n",
       "23   2016-01-06 10:30:00     1.07328  Market Sell   -0.00018   -0.18  0.015   \n",
       "24   2016-01-06 14:00:00     1.07396  Market Sell    0.00039    0.39  0.015   \n",
       "25   2016-01-06 14:30:00     1.07392  Market Sell   -0.00048   -0.48  0.015   \n",
       "26   2016-01-06 19:00:00     1.07558  Market Sell    0.00068    0.68  0.015   \n",
       "27   2016-01-06 22:00:00     1.07807  Market Sell    0.00260    2.60  0.015   \n",
       "28   2016-01-06 22:30:00     1.07805  Market Sell    0.00375    3.75  0.015   \n",
       "29   2016-01-06 23:00:00     1.07742  Market Sell    0.00312    3.12  0.015   \n",
       "..                   ...         ...          ...        ...     ...    ...   \n",
       "554                 None         NaN         None    0.01052   10.52  0.015   \n",
       "555                 None         NaN         None    0.00974    9.74  0.015   \n",
       "556                 None         NaN         None    0.00933    9.33  0.015   \n",
       "557                 None         NaN         None    0.00887    8.87  0.015   \n",
       "558                 None         NaN         None    0.00878    8.78  0.015   \n",
       "559                 None         NaN         None    0.01065   10.65  0.015   \n",
       "560                 None         NaN         None    0.01076   10.76  0.015   \n",
       "561                 None         NaN         None    0.01139   11.39  0.015   \n",
       "562                 None         NaN         None    0.01118   11.18  0.015   \n",
       "563                 None         NaN         None    0.00724    7.24  0.015   \n",
       "564                 None         NaN         None    0.00658    6.58  0.015   \n",
       "565                 None         NaN         None    0.00412    4.12  0.015   \n",
       "566                 None         NaN         None    0.00361    3.61  0.015   \n",
       "567                 None         NaN         None    0.00378    3.78  0.015   \n",
       "568                 None         NaN         None    0.00172    1.72  0.015   \n",
       "569                 None         NaN         None    0.00151    1.51  0.015   \n",
       "570                 None         NaN         None    0.00271    2.71  0.015   \n",
       "571                 None         NaN         None    0.00192    1.92  0.015   \n",
       "572                 None         NaN         None   -0.00213   -2.13  0.015   \n",
       "573                 None         NaN         None   -0.00037   -0.37  0.015   \n",
       "574                 None         NaN         None    0.00105    1.05  0.015   \n",
       "575                 None         NaN         None    0.00108    1.08  0.015   \n",
       "576                 None         NaN         None    0.00015    0.15  0.015   \n",
       "577                 None         NaN         None   -0.00061   -0.61  0.015   \n",
       "578                 None         NaN         None   -0.00124   -1.24  0.015   \n",
       "579                 None         NaN         None   -0.00219   -2.19  0.015   \n",
       "580                 None         NaN         None   -0.00131   -1.31  0.015   \n",
       "581                 None         NaN         None   -0.00034   -0.34  0.015   \n",
       "582                 None         NaN         None   -0.00081   -0.81  0.015   \n",
       "583                 None         NaN         None   -0.00019   -0.19  0.015   \n",
       "\n",
       "                 holding_period  drawdown    run_up  returns_diff  \n",
       "0                       1:30:00  0.001791  0.000378     -0.000997  \n",
       "1                       1:30:00  0.000887  0.000924     -0.000721  \n",
       "2                       3:00:00  0.001709  0.001294     -0.001497  \n",
       "3                       3:00:00  0.002355  0.000646     -0.001921  \n",
       "4                       3:00:00  0.004210  0.000499     -0.002327  \n",
       "5                       3:00:00  0.004377  0.000600     -0.003721  \n",
       "6                       3:00:00  0.005034  0.000351     -0.003454  \n",
       "7                       2:30:00  0.000214  0.001220      0.000130  \n",
       "8                       2:30:00  0.001182  0.000251     -0.000195  \n",
       "9                       6:30:00  0.000996  0.001359      0.000065  \n",
       "10                      6:30:00  0.000019  0.003689      0.001062  \n",
       "11                      5:30:00  0.000428  0.002718      0.002197  \n",
       "12                      7:00:00  0.000995  0.002149      0.000158  \n",
       "13                      7:00:00  0.001033  0.002112     -0.000437  \n",
       "14                      7:00:00  0.000698  0.002447      0.000177  \n",
       "15                      7:00:00  0.001153  0.001990     -0.000223  \n",
       "16                      7:00:00  0.001358  0.001785     -0.000874  \n",
       "17                      7:00:00  0.001506  0.001636     -0.001023  \n",
       "18                      7:00:00  0.001098  0.002046      0.000084  \n",
       "19                      6:00:00  0.003931  0.000836     -0.002546  \n",
       "20                      6:00:00  0.004171  0.000595     -0.003177  \n",
       "21                      6:00:00  0.004162  0.000557     -0.002982  \n",
       "22                      2:30:00  0.002392  0.000949     -0.000875  \n",
       "23                      1:00:00  0.001099  0.000307     -0.000168  \n",
       "24                      3:30:00  0.001910  0.002263      0.000363  \n",
       "25                      3:30:00  0.002681  0.001489     -0.000447  \n",
       "26                      7:30:00  0.003144  0.004689      0.000633  \n",
       "27                     10:00:00  0.003673  0.004156      0.002418  \n",
       "28                     10:00:00  0.002588  0.005250      0.003491  \n",
       "29                     10:00:00  0.002588  0.005250      0.002904  \n",
       "..                          ...       ...       ...           ...  \n",
       "554   1008 days, 3:14:24.250020  0.000596  0.031196      0.027563  \n",
       "555   1008 days, 1:44:24.252845  0.000596  0.031196      0.027563  \n",
       "556   1008 days, 1:14:24.255557  0.000596  0.031196      0.027563  \n",
       "557  1007 days, 22:44:24.258263  0.000596  0.031196      0.027563  \n",
       "558  1007 days, 22:14:24.261205  0.000596  0.031196      0.027563  \n",
       "559  1007 days, 21:44:24.264659  0.000596  0.031196      0.027563  \n",
       "560  1007 days, 19:14:24.267997  0.000596  0.031196      0.027563  \n",
       "561  1007 days, 18:44:24.271346  0.000596  0.031196      0.027563  \n",
       "562  1007 days, 18:14:24.274196  0.000596  0.031196      0.027563  \n",
       "563  1007 days, 14:44:24.276903  0.000596  0.031196      0.027563  \n",
       "564  1007 days, 14:14:24.279598  0.000596  0.031196      0.027563  \n",
       "565  1007 days, 13:44:24.282315  0.000596  0.031196      0.027563  \n",
       "566  1007 days, 13:14:24.285016  0.000596  0.031196      0.027563  \n",
       "567  1007 days, 12:44:24.287718  0.000596  0.031196      0.027563  \n",
       "568  1007 days, 12:14:24.290421  0.000596  0.031196      0.027563  \n",
       "569  1007 days, 11:44:24.293186  0.000596  0.031196      0.027563  \n",
       "570  1007 days, 11:14:24.295881  0.000596  0.031196      0.027563  \n",
       "571  1007 days, 10:44:24.298576  0.000596  0.031196      0.027563  \n",
       "572   1007 days, 9:44:24.301272  0.000596  0.031196      0.027563  \n",
       "573   1007 days, 9:14:24.304001  0.000596  0.031196      0.027563  \n",
       "574   1007 days, 8:44:24.306750  0.000596  0.031196      0.027563  \n",
       "575   1007 days, 5:44:24.309452  0.000596  0.031196      0.027563  \n",
       "576   1007 days, 5:14:24.312156  0.000596  0.031196      0.027563  \n",
       "577   1007 days, 4:44:24.314855  0.000596  0.031196      0.027563  \n",
       "578   1007 days, 4:14:24.317541  0.000596  0.031196      0.027563  \n",
       "579   1007 days, 3:44:24.320242  0.000596  0.031196      0.027563  \n",
       "580   1007 days, 3:14:24.322986  0.000596  0.031196      0.027563  \n",
       "581   1007 days, 2:44:24.325686  0.000596  0.031196      0.027563  \n",
       "582   1007 days, 1:14:24.328395  0.000596  0.031196      0.027563  \n",
       "583   1007 days, 0:44:24.331094  0.000596  0.031196      0.027563  \n",
       "\n",
       "[584 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "go.output.trade_log()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
